In terms of neuroimage analysis, brain fingerprinting methods have been used to investigate the variability of individuals based on neuroimage-specific preprocessing and data, e.g. brain segmentations from structural MRI [1]}, [2]}, neuroanatomic parcellations and functional MRI [3]}, fiber tracts from diffusion MRI [4]}. None of these methods have been sufficiently specific to achieve perfect accuracy at individual identification, and none have been used for the more difficult task of predicting family members. Heritability studies have investigated correlations in MRIs of siblings, including structural [5]}, [6]} and functional MRI [7]}, however these are not designed for prediction. In contrast, the keypoint methodology we adopt may be applied to generic imaging data with no preprocessing, representing distinctive patterns potentially present only in images of specific individuals or immediate family members. Our work here extends and compares against the keypoint-based neuroimage signatures method [8]}, the only method reporting perfect accuracy at same-subject image prediction from standard neuroimage datasets (e.g. OASIS [9]}, ADNI [10]}, HCP [11]}).
